See here for a complete list.

Some recent highlights:

Schüz, S., & Zarrieß, S. (2021). Diversity as a By-Product: Goal-oriented Language Generation Leads to Linguistic Variation. Proceedings of the 22nd Annual SIGdial Meeting on Discourse and Dialogue, Forthcoming. Association for Computational Linguistics.PUB

Giesen, J., Kahlmeyer, P., Laue, S., Mitterreiter, M., Nussbaum, F., Staudt, C., & Zarrieß, S. (2021). Method of Moments for Topic Models with Mixed Discrete and Continuous Features. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Forthcoming.PUB

Schüz, S., & Zarrieß, S. (2020). Knowledge Supports Visual Language Grounding: A Case Study on Colour Terms. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 6536-6542. Stroudsburg, PA, USA: Association for Computational Linguistics. DOI PUB

Silberer, C., Zarrieß, S., Westera, M., & Boleda, G. (2020). Humans Meet Models on Object Naming: A New Dataset and Analysis. Proceedings of the 28th International Conference on Computational Linguistics, 1893-1905. Stroudsburg, PA, USA: International Committee on Computational Linguistics. DOI PUB

Zarrieß, S., & Schlangen, D. (2019). Know What You Don’t Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 654-659. Stroudsburg, PA, USA: Association for Computational Linguistics.DOI PUB

Zarrieß, S., & Schlangen, D. (2018). Decoding Strategies for Neural Referring Expression Generation. Proceedings of INLG 2018. PUB